In real-world data science applications, data structures play a crucial role in efficiently storing, organizing, and manipulating data. Common structures like arrays, lists, stacks, queues, hash tables, trees, and graphs are used to optimize tasks like searching, sorting, and data retrieval. For instance, hash tables enable fast lookups in large datasets, while trees (such as decision trees) are used for machine learning models. Graphs model complex relationships, such as social networks. Choosing the right data structure can drastically improve algorithm performance and is critical in applications like recommendation systems, natural language processing, and big data analytics.
